Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39632
Title: The analysis and applications of adaptive-binning color histograms
Authors: Leow, W.K. 
Li, R. 
Keywords: Adaptive binning
Color histograms
Histogram-based dissimilarity measures
Image classification
Image clustering
Image retrieval
Issue Date: 2004
Source: Leow, W.K.,Li, R. (2004). The analysis and applications of adaptive-binning color histograms. Computer Vision and Image Understanding 94 (1-3) : 67-91. ScholarBank@NUS Repository.
Abstract: Histograms are commonly used in content-based image retrieval systems to represent the distributions of colors in images. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than do histograms with fixed binnings. However, existing systems almost exclusively adopt fixed-binning histograms because, among existing well-known dissimilarity measures, only the computationally expensive Earth Mover's Distance (EMD) can compare histograms with different binnings. This paper addresses the issue by defining a new dissimilarity measure that is more reliable than the Euclidean distance and yet computationally less expensive than EMD. Moreover, a mathematically sound definition of mean histogram can be defined for histogram clustering applications. Extensive test results show that adaptive histograms produce the best overall performance, in terms of good accuracy, small number of bins, no empty bin, and efficient computation, compared to existing methods for histogram retrieval, classification, and clustering tasks. © 2003 Elsevier Inc. All rights reserved.
Source Title: Computer Vision and Image Understanding
URI: http://scholarbank.nus.edu.sg/handle/10635/39632
ISSN: 10773142
Appears in Collections:Staff Publications

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